class_docstr: Policy that derives its actions from a learned Q function.
  
  The action returned is the one that yields the highest Q value for
  a given state, as determined by the supplied Q function.
  
  Args:
      qf (object): Q network.
      env_spec (EnvSpec): Environment specification.
      name (str): Name of this policy.
docstr: Get a single action given an observation.
  
  Args:
      observation (np.ndarray): Observation with shape :math:`(O, )`.
  
  Returns:
      torch.Tensor: Predicted action with shape :math:`(A, )`.
      dict: Empty since this policy does not produce a distribution.
docstr_tok: ["Get","a","single","action","given","an","observation",".","Args",":","observation","(","np.ndarray",")",":","Observation","with","shape",":","math",":","`","(","O",",",")","`",".","Returns",":","torch.Tensor",":","Predicted","action","with","shape",":","math",":","`","(","A",",",")","`",".","dict",":","Empty","since","this","policy","does","not","produce","a","distribution","."]
code: def get_action(self, observation):
      """Get a single action given an observation.
  
      Args:
          observation (np.ndarray): Observation with shape :math:`(O, )`.
  
      Returns:
          torch.Tensor: Predicted action with shape :math:`(A, )`.
          dict: Empty since this policy does not produce a distribution.
      """
      act, info = self.get_actions(np.expand_dims(observation, axis=0))
      return act[0], info
  
file_path: garage/src/garage/torch/policies/discrete_qf_argmax_policy.py
imports: import numpy
  import torch
  from garage.torch import np_to_torch
  from garage.torch.policies.policy import Policy
label_desc: Use image node_id 3 for calling the DiscreteQFArgmaxPolicy obj's underlying member method code with example usage: obj.get_action(observation) and returns: act,  info